USE OF ARTIFICIAL INTELLIGENCE METHODS AND MODELS FOR IMPROVING EXPERT SYSTEMS OF INTRUSION DETECTION

Abstract

In the domain of cyber security, the efficacy of Intrusion Detection Systems (IDS) is critical for the proactive identification and mitigation of cyber threats. This research delineates a novel paradigm for enhancing IDS accuracy through the integration of advanced Artificial Intelligence (AI) methodologies, thereby setting a new benchmark in network security defense mechanisms. Utilizing a synergistic approach that combines both descriptive and inferential statistical analyses, this study introduces an expert system endowed with the capability to detect network intrusions with an unparalleled accuracy rate of 99.98%. By incorporating Extreme Gradient Boosting (XGBoost) for the classification of predefined attack vectors and a Variational Autoencoder (VAE) for anomaly detection, the research extends the boundaries of current cyber threat detection frameworks. These methodologies not only enhance the precision of threat categorization but also introduce a mechanism for the system to adapt to novel, previously unidentified cyber threats through real-time learning and adaptation to emerging data patterns. Critically, the expert system is engineered to facilitate high-speed data processing and supports online learning, making it optimally suited for application in high-traffic network environments. The scientific novelty of this research is encapsulated in the formulation of advanced AI-driven models for the dual purposes of traffic anomaly detection and the classification of cyber-attack types based on distinctive behavioral characteristics. These models are meticulously designed to evolve, learning from new data in real time, thereby continuously enhancing the system's efficacy. In practical terms, the system provides a robust solution for the protection of digital ecosystems against intrusions, enabling the automatic filtration of malicious network traffic. Beyond its immediate applicability, the study contributes to the field of cyber security by laying down a foundational framework for the future exploration of AI-based security solutions. It invites further scientific inquiry into the development of adaptive, intelligent IDS mechanisms, potentially revolutionizing the approach to cyber defense strategies.